Introduction.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"xmXI8Qq17FJT"},"source":["<h1>Introduction</h1>\n","<h3>Welcome!</h3>\n","\n","<p>\n","In this section, you will learn how to approach data acquisition in various ways, and obtain necessary insights from a dataset. By the end of this lab, you will successfully load the data into Jupyter Notebook, and gain some fundamental insights via Pandas Library.\n","</p>"]},{"cell_type":"markdown","metadata":{"id":"yqFZH2I87FJW"},"source":["<h2>Table of Contents</h2>\n","\n","<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n","<ol>\n","    <li><a href=\"#data_acquisition\">Data Acquisition</a>\n","    <li><a href=\"#basic_insight\">Basic Insight of Dataset</a></li>\n","</ol>\n","\n","Estimated Time Needed: <strong>10 min</strong>\n","</div>\n","<hr>"]},{"cell_type":"markdown","metadata":{"id":"zj0gYY8g7FJa"},"source":["<h1 id=\"data_acquisition\">Data Acquisition</h1>\n","<p>\n","There are various formats for a dataset, .csv, .json, .xlsx  etc. The dataset can be stored in different places, on your local machine or sometimes online.<br>\n","In this section, you will learn how to load a dataset into our Jupyter Notebook.<br>\n","In our case, the Automobile Dataset is an online source, and it is in CSV (comma separated value) format. Let's use this dataset as an example to practice data reading.\n","<ul>\n","    <li>data source: <a href=\"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\" target=\"_blank\">https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data</a></li>\n","    <li>data type: csv</li>\n","</ul>\n","The Pandas Library is a useful tool that enables us to read various datasets into a data frame; our Jupyter notebook platforms have a built-in <b>Pandas Library</b> so that all we need to do is import Pandas without installing.\n","</p>"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":72},"executionInfo":{"elapsed":1623,"status":"ok","timestamp":1590576055454,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjO-5mwIGmIuVq8cICuMfXOg-vcdmRnOxVKCuNt6Ws=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"PB946sEz7FJf","outputId":"b54afa6d-6163-44de-885c-b32721591058"},"outputs":[],"source":["# import prerequisite libraries\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import os\n","import seaborn as sns"]},{"cell_type":"markdown","metadata":{"id":"0_aMnwM17FJm"},"source":["<h2>Read Data</h2>\n","<p>\n","We use <code>pandas.read_csv()</code> function to read the csv file. In the bracket, we put the file path along with a quotation mark, so that pandas will read the file into a data frame from that address. The file path can be either an URL or your local file address.<br>\n","Because the data does not include headers, we can add an argument <code>headers = None</code>  inside the  <code>read_csv()</code> method, so that pandas will not automatically set the first row as a header.<br>\n","You can also assign the dataset to any variable you create.\n","</p>"]},{"cell_type":"markdown","metadata":{"id":"tlq3yQBJ7FJo"},"source":["This dataset was hosted on IBM Cloud object click <a href=\"https://cocl.us/cognitive_class_DA0101EN_objectstorage\">HERE</a> for free storage."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_9tsYzAo7FJp"},"outputs":[],"source":["# filename = \"imports-85.data\"\n","# df = pd.re|ad_csv(filename,header=None)\n","\n","#url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\"\n","#df = pd.read_csv(url,header=None)\n","\n","df=pd.read_csv('laptop_data.csv')"]},{"cell_type":"markdown","metadata":{"id":"PHi2qP7I7FJw"},"source":["After reading the dataset, we can use the <code>dataframe.head(n)</code> method to check the top n rows of the dataframe; where n is an integer. Contrary to <code>dataframe.head(n)</code>, <code>dataframe.tail(n)</code> will show you the bottom n rows of the dataframe.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":213},"executionInfo":{"elapsed":1118,"status":"ok","timestamp":1590576182734,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjO-5mwIGmIuVq8cICuMfXOg-vcdmRnOxVKCuNt6Ws=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"IKms6K6c7FJy","outputId":"68b8cdb3-378c-4ddd-cf7e-e81228d32682"},"outputs":[{"name":"stdout","output_type":"stream","text":["The first 5 rows of the dataframe\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","      <th>25</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>convertible</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>88.6</td>\n","      <td>168.8</td>\n","      <td>64.1</td>\n","      <td>48.8</td>\n","      <td>2548</td>\n","      <td>dohc</td>\n","      <td>four</td>\n","      <td>130</td>\n","      <td>mpfi</td>\n","      <td>3.47</td>\n","      <td>2.68</td>\n","      <td>9.0</td>\n","      <td>111</td>\n","      <td>5000</td>\n","      <td>21</td>\n","      <td>27</td>\n","      <td>13495</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>convertible</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>88.6</td>\n","      <td>168.8</td>\n","      <td>64.1</td>\n","      <td>48.8</td>\n","      <td>2548</td>\n","      <td>dohc</td>\n","      <td>four</td>\n","      <td>130</td>\n","      <td>mpfi</td>\n","      <td>3.47</td>\n","      <td>2.68</td>\n","      <td>9.0</td>\n","      <td>111</td>\n","      <td>5000</td>\n","      <td>21</td>\n","      <td>27</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>hatchback</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>94.5</td>\n","      <td>171.2</td>\n","      <td>65.5</td>\n","      <td>52.4</td>\n","      <td>2823</td>\n","      <td>ohcv</td>\n","      <td>six</td>\n","      <td>152</td>\n","      <td>mpfi</td>\n","      <td>2.68</td>\n","      <td>3.47</td>\n","      <td>9.0</td>\n","      <td>154</td>\n","      <td>5000</td>\n","      <td>19</td>\n","      <td>26</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>164</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>99.8</td>\n","      <td>176.6</td>\n","      <td>66.2</td>\n","      <td>54.3</td>\n","      <td>2337</td>\n","      <td>ohc</td>\n","      <td>four</td>\n","      <td>109</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>10.0</td>\n","      <td>102</td>\n","      <td>5500</td>\n","      <td>24</td>\n","      <td>30</td>\n","      <td>13950</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2</td>\n","      <td>164</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>4wd</td>\n","      <td>front</td>\n","      <td>99.4</td>\n","      <td>176.6</td>\n","      <td>66.4</td>\n","      <td>54.3</td>\n","      <td>2824</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.0</td>\n","      <td>115</td>\n","      <td>5500</td>\n","      <td>18</td>\n","      <td>22</td>\n","      <td>17450</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   0    1            2    3    4     5   ...    20   21    22  23  24     25\n","0   3    ?  alfa-romero  gas  std   two  ...   9.0  111  5000  21  27  13495\n","1   3    ?  alfa-romero  gas  std   two  ...   9.0  111  5000  21  27  16500\n","2   1    ?  alfa-romero  gas  std   two  ...   9.0  154  5000  19  26  16500\n","3   2  164         audi  gas  std  four  ...  10.0  102  5500  24  30  13950\n","4   2  164         audi  gas  std  four  ...   8.0  115  5500  18  22  17450\n","\n","[5 rows x 26 columns]"]},"execution_count":3,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["# show the first 5 rows using dataframe.head() method\n","print(\"The first 5 rows of the dataframe\") \n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"JJpRv8KH7FJ3"},"source":["<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n","<h1> Question #1: </h1>\n","<b>check the bottom 10 rows of data frame \"df\".</b>\n","</div>"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AIjwwYBx7FJ5"},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n","df.tail()"]},{"cell_type":"markdown","metadata":{"id":"lhNIHz_U7FKC"},"source":["<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n","<h1> Question #1 Answer: </h1>\n","<b>Run the code below for the solution!</b>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"-ONL9Xf37FKD"},"source":["Double-click <b>here</b> for the solution.\n","\n","<!-- The answer is below:\n","\n","print(\"The last 10 rows of the dataframe\\n\")\n","df.tail(10)\n","\n","-->"]},{"cell_type":"markdown","metadata":{"id":"XYcyItML7FKF"},"source":["<h3>Add Headers</h3>\n","<p>\n","Take a look at our dataset; pandas automatically set the header by an integer from 0.\n","</p>\n","<p>\n","To better describe our data we can introduce a header, this information is available at:  <a href=\"https://archive.ics.uci.edu/ml/datasets/Automobile\" target=\"_blank\">https://archive.ics.uci.edu/ml/datasets/Automobile</a>\n","</p>\n","<p>\n","Thus, we have to add headers manually.\n","</p>\n","<p>\n","Firstly, we create a list \"headers\" that include all column names in order.\n","Then, we use <code>dataframe.columns = headers</code> to replace the headers by the list we created.\n","</p>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":71},"executionInfo":{"elapsed":3784,"status":"ok","timestamp":1579409272240,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"A6BBYQPM7FKG","outputId":"288626c4-18d3-43a2-eeaf-5bf5a2130f93"},"outputs":[{"name":"stdout","output_type":"stream","text":["headers\n"," ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n"]}],"source":["# create headers list\n","headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\n","           \"aspiration\", \"num-of-doors\",\"body-style\",\"drive-wheels\",\n","           \"engine-location\",\"wheel-base\", \"length\",\"width\",\n","           \"height\",\"curb-weight\",\"engine-type\",\"num-of-cylinders\",\n","           \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\n","           \"compression-ratio\",\"horsepower\",\"peak-rpm\",\"city-mpg\",\n","           \"highway-mpg\",\"price\"]\n","print(\"headers\\n\", headers)"]},{"cell_type":"markdown","metadata":{"id":"xowN0Zob7FKM"},"source":[" We replace headers and recheck our data frame"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df #show the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.columns # check the column names"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":463},"executionInfo":{"elapsed":3762,"status":"ok","timestamp":1579409272241,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"Ocfd1gVq7FKP","outputId":"9ea5143a-05d5-4445-8e54-e80f388cec7e"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>symboling</th>\n","      <th>normalized-losses</th>\n","      <th>make</th>\n","      <th>fuel-type</th>\n","      <th>aspiration</th>\n","      <th>num-of-doors</th>\n","      <th>body-style</th>\n","      <th>drive-wheels</th>\n","      <th>engine-location</th>\n","      <th>wheel-base</th>\n","      <th>length</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>curb-weight</th>\n","      <th>engine-type</th>\n","      <th>num-of-cylinders</th>\n","      <th>engine-size</th>\n","      <th>fuel-system</th>\n","      <th>bore</th>\n","      <th>stroke</th>\n","      <th>compression-ratio</th>\n","      <th>horsepower</th>\n","      <th>peak-rpm</th>\n","      <th>city-mpg</th>\n","      <th>highway-mpg</th>\n","      <th>price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>convertible</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>88.6</td>\n","      <td>168.8</td>\n","      <td>64.1</td>\n","      <td>48.8</td>\n","      <td>2548</td>\n","      <td>dohc</td>\n","      <td>four</td>\n","      <td>130</td>\n","      <td>mpfi</td>\n","      <td>3.47</td>\n","      <td>2.68</td>\n","      <td>9.0</td>\n","      <td>111</td>\n","      <td>5000</td>\n","      <td>21</td>\n","      <td>27</td>\n","      <td>13495</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>convertible</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>88.6</td>\n","      <td>168.8</td>\n","      <td>64.1</td>\n","      <td>48.8</td>\n","      <td>2548</td>\n","      <td>dohc</td>\n","      <td>four</td>\n","      <td>130</td>\n","      <td>mpfi</td>\n","      <td>3.47</td>\n","      <td>2.68</td>\n","      <td>9.0</td>\n","      <td>111</td>\n","      <td>5000</td>\n","      <td>21</td>\n","      <td>27</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>hatchback</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>94.5</td>\n","      <td>171.2</td>\n","      <td>65.5</td>\n","      <td>52.4</td>\n","      <td>2823</td>\n","      <td>ohcv</td>\n","      <td>six</td>\n","      <td>152</td>\n","      <td>mpfi</td>\n","      <td>2.68</td>\n","      <td>3.47</td>\n","      <td>9.0</td>\n","      <td>154</td>\n","      <td>5000</td>\n","      <td>19</td>\n","      <td>26</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>164</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>99.8</td>\n","      <td>176.6</td>\n","      <td>66.2</td>\n","      <td>54.3</td>\n","      <td>2337</td>\n","      <td>ohc</td>\n","      <td>four</td>\n","      <td>109</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>10.0</td>\n","      <td>102</td>\n","      <td>5500</td>\n","      <td>24</td>\n","      <td>30</td>\n","      <td>13950</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2</td>\n","      <td>164</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>4wd</td>\n","      <td>front</td>\n","      <td>99.4</td>\n","      <td>176.6</td>\n","      <td>66.4</td>\n","      <td>54.3</td>\n","      <td>2824</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.0</td>\n","      <td>115</td>\n","      <td>5500</td>\n","      <td>18</td>\n","      <td>22</td>\n","      <td>17450</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2</td>\n","      <td>?</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>99.8</td>\n","      <td>177.3</td>\n","      <td>66.3</td>\n","      <td>53.1</td>\n","      <td>2507</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.5</td>\n","      <td>110</td>\n","      <td>5500</td>\n","      <td>19</td>\n","      <td>25</td>\n","      <td>15250</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1</td>\n","      <td>158</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>105.8</td>\n","      <td>192.7</td>\n","      <td>71.4</td>\n","      <td>55.7</td>\n","      <td>2844</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.5</td>\n","      <td>110</td>\n","      <td>5500</td>\n","      <td>19</td>\n","      <td>25</td>\n","      <td>17710</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>?</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>wagon</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>105.8</td>\n","      <td>192.7</td>\n","      <td>71.4</td>\n","      <td>55.7</td>\n","      <td>2954</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.5</td>\n","      <td>110</td>\n","      <td>5500</td>\n","      <td>19</td>\n","      <td>25</td>\n","      <td>18920</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>158</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>turbo</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>105.8</td>\n","      <td>192.7</td>\n","      <td>71.4</td>\n","      <td>55.9</td>\n","      <td>3086</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>131</td>\n","      <td>mpfi</td>\n","      <td>3.13</td>\n","      <td>3.40</td>\n","      <td>8.3</td>\n","      <td>140</td>\n","      <td>5500</td>\n","      <td>17</td>\n","      <td>20</td>\n","      <td>23875</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0</td>\n","      <td>?</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>turbo</td>\n","      <td>two</td>\n","      <td>hatchback</td>\n","      <td>4wd</td>\n","      <td>front</td>\n","      <td>99.5</td>\n","      <td>178.2</td>\n","      <td>67.9</td>\n","      <td>52.0</td>\n","      <td>3053</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>131</td>\n","      <td>mpfi</td>\n","      <td>3.13</td>\n","      <td>3.40</td>\n","      <td>7.0</td>\n","      <td>160</td>\n","      <td>5500</td>\n","      <td>16</td>\n","      <td>22</td>\n","      <td>?</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   symboling normalized-losses         make  ... city-mpg highway-mpg  price\n","0          3                 ?  alfa-romero  ...       21          27  13495\n","1          3                 ?  alfa-romero  ...       21          27  16500\n","2          1                 ?  alfa-romero  ...       19          26  16500\n","3          2               164         audi  ...       24          30  13950\n","4          2               164         audi  ...       18          22  17450\n","5          2                 ?         audi  ...       19          25  15250\n","6          1               158         audi  ...       19          25  17710\n","7          1                 ?         audi  ...       19          25  18920\n","8          1               158         audi  ...       17          20  23875\n","9          0                 ?         audi  ...       16          22      ?\n","\n","[10 rows x 26 columns]"]},"execution_count":5,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df.columns = headers\n","df.head(10)"]},{"cell_type":"markdown","metadata":{"id":"sNK9LNcR7FKl"},"source":["Now, we have successfully read the raw dataset and add the correct headers into the data frame."]},{"cell_type":"markdown","metadata":{"id":"F9e7EBMf7FKn"},"source":[" <div>\n","<b>Find the name of the columns of the dataframe</b>\n","</div>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":137},"executionInfo":{"elapsed":3734,"status":"ok","timestamp":1579409272243,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"_Vs1K2vE7FKp","outputId":"b62f445d-8b2d-4cf1-fa16-161a427612bb"},"outputs":[{"data":{"text/plain":["Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n","       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n","       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n","       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n","       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n","       'highway-mpg', 'price'],\n","      dtype='object')"]},"execution_count":6,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["# Write your code below and press Shift+Enter to execute \n","df.columns"]},{"cell_type":"markdown","metadata":{"id":"Rsk3_2857FKv"},"source":["Double-click <b>here</b> for the solution.\n","\n","<!-- The answer is below:\n","\n","print(df.columns)\n","\n","-->"]},{"cell_type":"markdown","metadata":{"id":"3fvN5ZI57FKw"},"source":["<h2>Save Dataset</h2>\n","<p>\n","Correspondingly, Pandas enables us to save the dataset to csv  by using the <code>dataframe.to_csv()</code> method, you can add the file path and name along with quotation marks in the brackets.\n","</p>\n","<p>\n","    For example, if you would save the dataframe <b>df</b> as <b>automobile.csv</b> to your local machine, you may use the syntax below:\n","</p>"]},{"cell_type":"markdown","metadata":{"id":"Lja_yGeq7FK1"},"source":["df.to_csv(\"automobile.csv\", index=False)"]},{"cell_type":"markdown","metadata":{"id":"3lStTc567FK2"},"source":[" We can also read and save other file formats, we can use similar functions to **`pd.read_csv()`** and **`df.to_csv()`** for other data formats, the functions are listed in the following table:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UDeBRZK27FKd"},"outputs":[],"source":["df.to_csv('dataset.csv')"]},{"cell_type":"markdown","metadata":{"id":"8-J66nor7FK4"},"source":["<h2>Read/Save Other Data Formats</h2>\n","\n","\n","\n","| Data Formate  | Read           | Save             |\n","| ------------- |:--------------:| ----------------:|\n","| csv           | `pd.read_csv()`  |`df.to_csv()`     |\n","| json          | `pd.read_json()` |`df.to_json()`    |\n","| excel         | `pd.read_excel()`|`df.to_excel()`   |\n","| hdf           | `pd.read_hdf()`  |`df.to_hdf()`     |\n","| sql           | `pd.read_sql()`  |`df.to_sql()`     |\n","| ...           |   ...          |       ...        |"]},{"cell_type":"markdown","metadata":{"id":"U-j6mC6c7FK5"},"source":["<h1 id=\"basic_insight\">Basic Insight of Dataset</h1>\n","<p>\n","After reading data into Pandas dataframe, it is time for us to explore the dataset.<br>\n","There are several ways to obtain essential insights of the data to help us better understand our dataset.\n","</p>"]},{"cell_type":"markdown","metadata":{"id":"vX9j4ZAs7FK8"},"source":["<h2>Data Types</h2>\n","<p>\n","Data has a variety of types.<br>\n","The main types stored in Pandas dataframes are <b>object</b>, <b>float</b>, <b>int</b>, <b>bool</b> and <b>datetime64</b>. In order to better learn about each attribute, it is always good for us to know the data type of each column. In Pandas:\n","</p>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":480},"executionInfo":{"elapsed":3699,"status":"ok","timestamp":1579409272246,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"Nz1kcS1K7FK_","outputId":"3a74af9d-f571-406d-e7eb-1dec4bc6aff6"},"outputs":[{"data":{"text/plain":["symboling              int64\n","normalized-losses     object\n","make                  object\n","fuel-type             object\n","aspiration            object\n","num-of-doors          object\n","body-style            object\n","drive-wheels          object\n","engine-location       object\n","wheel-base           float64\n","length               float64\n","width                float64\n","height               float64\n","curb-weight            int64\n","engine-type           object\n","num-of-cylinders      object\n","engine-size            int64\n","fuel-system           object\n","bore                  object\n","stroke                object\n","compression-ratio    float64\n","horsepower            object\n","peak-rpm              object\n","city-mpg               int64\n","highway-mpg            int64\n","price                 object\n","dtype: object"]},"execution_count":8,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df.dtypes # check the data type of data frame \"df\" by .dtypes\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":463},"executionInfo":{"elapsed":3678,"status":"ok","timestamp":1579409272248,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"C7u8lA5ifNEa","outputId":"3b22d490-ddf1-44d8-de05-e90bf3f028a2"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>symboling</th>\n","      <th>normalized-losses</th>\n","      <th>make</th>\n","      <th>fuel-type</th>\n","      <th>aspiration</th>\n","      <th>num-of-doors</th>\n","      <th>body-style</th>\n","      <th>drive-wheels</th>\n","      <th>engine-location</th>\n","      <th>wheel-base</th>\n","      <th>length</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>curb-weight</th>\n","      <th>engine-type</th>\n","      <th>num-of-cylinders</th>\n","      <th>engine-size</th>\n","      <th>fuel-system</th>\n","      <th>bore</th>\n","      <th>stroke</th>\n","      <th>compression-ratio</th>\n","      <th>horsepower</th>\n","      <th>peak-rpm</th>\n","      <th>city-mpg</th>\n","      <th>highway-mpg</th>\n","      <th>price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>convertible</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>88.6</td>\n","      <td>168.8</td>\n","      <td>64.1</td>\n","      <td>48.8</td>\n","      <td>2548</td>\n","      <td>dohc</td>\n","      <td>four</td>\n","      <td>130</td>\n","      <td>mpfi</td>\n","      <td>3.47</td>\n","      <td>2.68</td>\n","      <td>9.0</td>\n","      <td>111</td>\n","      <td>5000</td>\n","      <td>21</td>\n","      <td>27</td>\n","      <td>13495</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>convertible</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>88.6</td>\n","      <td>168.8</td>\n","      <td>64.1</td>\n","      <td>48.8</td>\n","      <td>2548</td>\n","      <td>dohc</td>\n","      <td>four</td>\n","      <td>130</td>\n","      <td>mpfi</td>\n","      <td>3.47</td>\n","      <td>2.68</td>\n","      <td>9.0</td>\n","      <td>111</td>\n","      <td>5000</td>\n","      <td>21</td>\n","      <td>27</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>?</td>\n","      <td>alfa-romero</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>hatchback</td>\n","      <td>rwd</td>\n","      <td>front</td>\n","      <td>94.5</td>\n","      <td>171.2</td>\n","      <td>65.5</td>\n","      <td>52.4</td>\n","      <td>2823</td>\n","      <td>ohcv</td>\n","      <td>six</td>\n","      <td>152</td>\n","      <td>mpfi</td>\n","      <td>2.68</td>\n","      <td>3.47</td>\n","      <td>9.0</td>\n","      <td>154</td>\n","      <td>5000</td>\n","      <td>19</td>\n","      <td>26</td>\n","      <td>16500</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>164</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>99.8</td>\n","      <td>176.6</td>\n","      <td>66.2</td>\n","      <td>54.3</td>\n","      <td>2337</td>\n","      <td>ohc</td>\n","      <td>four</td>\n","      <td>109</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>10.0</td>\n","      <td>102</td>\n","      <td>5500</td>\n","      <td>24</td>\n","      <td>30</td>\n","      <td>13950</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2</td>\n","      <td>164</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>4wd</td>\n","      <td>front</td>\n","      <td>99.4</td>\n","      <td>176.6</td>\n","      <td>66.4</td>\n","      <td>54.3</td>\n","      <td>2824</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.0</td>\n","      <td>115</td>\n","      <td>5500</td>\n","      <td>18</td>\n","      <td>22</td>\n","      <td>17450</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2</td>\n","      <td>?</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>two</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>99.8</td>\n","      <td>177.3</td>\n","      <td>66.3</td>\n","      <td>53.1</td>\n","      <td>2507</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.5</td>\n","      <td>110</td>\n","      <td>5500</td>\n","      <td>19</td>\n","      <td>25</td>\n","      <td>15250</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1</td>\n","      <td>158</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>105.8</td>\n","      <td>192.7</td>\n","      <td>71.4</td>\n","      <td>55.7</td>\n","      <td>2844</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.5</td>\n","      <td>110</td>\n","      <td>5500</td>\n","      <td>19</td>\n","      <td>25</td>\n","      <td>17710</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>?</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>wagon</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>105.8</td>\n","      <td>192.7</td>\n","      <td>71.4</td>\n","      <td>55.7</td>\n","      <td>2954</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>136</td>\n","      <td>mpfi</td>\n","      <td>3.19</td>\n","      <td>3.40</td>\n","      <td>8.5</td>\n","      <td>110</td>\n","      <td>5500</td>\n","      <td>19</td>\n","      <td>25</td>\n","      <td>18920</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>158</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>turbo</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>105.8</td>\n","      <td>192.7</td>\n","      <td>71.4</td>\n","      <td>55.9</td>\n","      <td>3086</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>131</td>\n","      <td>mpfi</td>\n","      <td>3.13</td>\n","      <td>3.40</td>\n","      <td>8.3</td>\n","      <td>140</td>\n","      <td>5500</td>\n","      <td>17</td>\n","      <td>20</td>\n","      <td>23875</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0</td>\n","      <td>?</td>\n","      <td>audi</td>\n","      <td>gas</td>\n","      <td>turbo</td>\n","      <td>two</td>\n","      <td>hatchback</td>\n","      <td>4wd</td>\n","      <td>front</td>\n","      <td>99.5</td>\n","      <td>178.2</td>\n","      <td>67.9</td>\n","      <td>52.0</td>\n","      <td>3053</td>\n","      <td>ohc</td>\n","      <td>five</td>\n","      <td>131</td>\n","      <td>mpfi</td>\n","      <td>3.13</td>\n","      <td>3.40</td>\n","      <td>7.0</td>\n","      <td>160</td>\n","      <td>5500</td>\n","      <td>16</td>\n","      <td>22</td>\n","      <td>?</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   symboling normalized-losses         make  ... city-mpg highway-mpg  price\n","0          3                 ?  alfa-romero  ...       21          27  13495\n","1          3                 ?  alfa-romero  ...       21          27  16500\n","2          1                 ?  alfa-romero  ...       19          26  16500\n","3          2               164         audi  ...       24          30  13950\n","4          2               164         audi  ...       18          22  17450\n","5          2                 ?         audi  ...       19          25  15250\n","6          1               158         audi  ...       19          25  17710\n","7          1                 ?         audi  ...       19          25  18920\n","8          1               158         audi  ...       17          20  23875\n","9          0                 ?         audi  ...       16          22      ?\n","\n","[10 rows x 26 columns]"]},"execution_count":9,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df.head(10)"]},{"cell_type":"markdown","metadata":{"id":"ZNgFVJhZ7FLE"},"source":["returns a Series with the data type of each column."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"elapsed":2613,"status":"ok","timestamp":1577110665887,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"KvL32Xnx7FLG","outputId":"b6a45c23-0db5-4443-e3a4-7c1ef7e29532","scrolled":false},"outputs":[{"name":"stdout","output_type":"stream","text":["object\n"]}],"source":["# check the data type of data frame \"df\" by .dtypes\n","print(df['price'].dtypes)"]},{"cell_type":"markdown","metadata":{"id":"DhGOfAW17FLL"},"source":["<p>\n","As a result, as shown above, it is clear to see that the data type of \"symboling\" and \"curb-weight\" are <code>int64</code>, \"normalized-losses\" is <code>object</code>, and \"wheel-base\" is <code>float64</code>, etc.\n","</p>\n","<p>\n","These data types can be changed; we will learn how to accomplish this in a later module.\n","</p>"]},{"cell_type":"markdown","metadata":{"id":"OSAWiSZj7FLN"},"source":["<h2>Describe</h2>\n","If we would like to get a statistical summary of each column, such as count, column mean value, column standard deviation, etc. We use the describe method:"]},{"cell_type":"markdown","metadata":{"id":"_BuGnlKP7FLP"},"source":["dataframe.describe()"]},{"cell_type":"markdown","metadata":{"id":"XMfcdJCf7FLR"},"source":["This method will provide various summary statistics, excluding <code>NaN</code> (Not a Number) values."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":284},"executionInfo":{"elapsed":1281,"status":"ok","timestamp":1590576907804,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjO-5mwIGmIuVq8cICuMfXOg-vcdmRnOxVKCuNt6Ws=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"W5SLEVgs7FLV","outputId":"e75b4331-fc2b-4ddd-f043-64e68cb11037"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>16</th>\n","      <th>20</th>\n","      <th>23</th>\n","      <th>24</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.834146</td>\n","      <td>98.756585</td>\n","      <td>174.049268</td>\n","      <td>65.907805</td>\n","      <td>53.724878</td>\n","      <td>2555.565854</td>\n","      <td>126.907317</td>\n","      <td>10.142537</td>\n","      <td>25.219512</td>\n","      <td>30.751220</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.245307</td>\n","      <td>6.021776</td>\n","      <td>12.337289</td>\n","      <td>2.145204</td>\n","      <td>2.443522</td>\n","      <td>520.680204</td>\n","      <td>41.642693</td>\n","      <td>3.972040</td>\n","      <td>6.542142</td>\n","      <td>6.886443</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-2.000000</td>\n","      <td>86.600000</td>\n","      <td>141.100000</td>\n","      <td>60.300000</td>\n","      <td>47.800000</td>\n","      <td>1488.000000</td>\n","      <td>61.000000</td>\n","      <td>7.000000</td>\n","      <td>13.000000</td>\n","      <td>16.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","      <td>94.500000</td>\n","      <td>166.300000</td>\n","      <td>64.100000</td>\n","      <td>52.000000</td>\n","      <td>2145.000000</td>\n","      <td>97.000000</td>\n","      <td>8.600000</td>\n","      <td>19.000000</td>\n","      <td>25.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.000000</td>\n","      <td>97.000000</td>\n","      <td>173.200000</td>\n","      <td>65.500000</td>\n","      <td>54.100000</td>\n","      <td>2414.000000</td>\n","      <td>120.000000</td>\n","      <td>9.000000</td>\n","      <td>24.000000</td>\n","      <td>30.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2.000000</td>\n","      <td>102.400000</td>\n","      <td>183.100000</td>\n","      <td>66.900000</td>\n","      <td>55.500000</td>\n","      <td>2935.000000</td>\n","      <td>141.000000</td>\n","      <td>9.400000</td>\n","      <td>30.000000</td>\n","      <td>34.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>3.000000</td>\n","      <td>120.900000</td>\n","      <td>208.100000</td>\n","      <td>72.300000</td>\n","      <td>59.800000</td>\n","      <td>4066.000000</td>\n","      <td>326.000000</td>\n","      <td>23.000000</td>\n","      <td>49.000000</td>\n","      <td>54.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               0           9           10  ...          20          23          24\n","count  205.000000  205.000000  205.000000  ...  205.000000  205.000000  205.000000\n","mean     0.834146   98.756585  174.049268  ...   10.142537   25.219512   30.751220\n","std      1.245307    6.021776   12.337289  ...    3.972040    6.542142    6.886443\n","min     -2.000000   86.600000  141.100000  ...    7.000000   13.000000   16.000000\n","25%      0.000000   94.500000  166.300000  ...    8.600000   19.000000   25.000000\n","50%      1.000000   97.000000  173.200000  ...    9.000000   24.000000   30.000000\n","75%      2.000000  102.400000  183.100000  ...    9.400000   30.000000   34.000000\n","max      3.000000  120.900000  208.100000  ...   23.000000   49.000000   54.000000\n","\n","[8 rows x 10 columns]"]},"execution_count":4,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"markdown","metadata":{"id":"FRXHRlbM7FLc"},"source":["<p>\n","This shows the statistical summary of all numeric-typed (int, float) columns.<br>\n","For example, the attribute \"symboling\" has 205 counts, the mean value of this column is 0.83, the standard deviation is 1.25, the minimum value is -2, 25th percentile is 0, 50th percentile is 1, 75th percentile is 2, and the maximum value is 3.\n","<br>\n","However, what if we would also like to check all the columns including those that are of type object.\n","<br><br>\n","\n","You can add an argument <code>include = \"all\"</code> inside the bracket. Let's try it again.\n","</p>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":3151,"status":"ok","timestamp":1577110666518,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"woI0ZG127FLg","outputId":"f6e180c7-d5df-4663-a38e-d7e774a747e8"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>symboling</th>\n","      <th>normalized-losses</th>\n","      <th>make</th>\n","      <th>fuel-type</th>\n","      <th>aspiration</th>\n","      <th>num-of-doors</th>\n","      <th>body-style</th>\n","      <th>drive-wheels</th>\n","      <th>engine-location</th>\n","      <th>wheel-base</th>\n","      <th>length</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>curb-weight</th>\n","      <th>engine-type</th>\n","      <th>num-of-cylinders</th>\n","      <th>engine-size</th>\n","      <th>fuel-system</th>\n","      <th>bore</th>\n","      <th>stroke</th>\n","      <th>compression-ratio</th>\n","      <th>horsepower</th>\n","      <th>peak-rpm</th>\n","      <th>city-mpg</th>\n","      <th>highway-mpg</th>\n","      <th>price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>205.000000</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205.000000</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205.000000</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","      <td>205</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>NaN</td>\n","      <td>52</td>\n","      <td>22</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>39</td>\n","      <td>37</td>\n","      <td>NaN</td>\n","      <td>60</td>\n","      <td>24</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>187</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>NaN</td>\n","      <td>?</td>\n","      <td>toyota</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>ohc</td>\n","      <td>four</td>\n","      <td>NaN</td>\n","      <td>mpfi</td>\n","      <td>3.62</td>\n","      <td>3.40</td>\n","      <td>NaN</td>\n","      <td>68</td>\n","      <td>5500</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>?</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>NaN</td>\n","      <td>41</td>\n","      <td>32</td>\n","      <td>185</td>\n","      <td>168</td>\n","      <td>114</td>\n","      <td>96</td>\n","      <td>120</td>\n","      <td>202</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>148</td>\n","      <td>159</td>\n","      <td>NaN</td>\n","      <td>94</td>\n","      <td>23</td>\n","      <td>20</td>\n","      <td>NaN</td>\n","      <td>19</td>\n","      <td>37</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.834146</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>98.756585</td>\n","      <td>174.049268</td>\n","      <td>65.907805</td>\n","      <td>53.724878</td>\n","      <td>2555.565854</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>126.907317</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>10.142537</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>25.219512</td>\n","      <td>30.751220</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.245307</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>6.021776</td>\n","      <td>12.337289</td>\n","      <td>2.145204</td>\n","      <td>2.443522</td>\n","      <td>520.680204</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>41.642693</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>3.972040</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>6.542142</td>\n","      <td>6.886443</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-2.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>86.600000</td>\n","      <td>141.100000</td>\n","      <td>60.300000</td>\n","      <td>47.800000</td>\n","      <td>1488.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>61.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>13.000000</td>\n","      <td>16.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>94.500000</td>\n","      <td>166.300000</td>\n","      <td>64.100000</td>\n","      <td>52.000000</td>\n","      <td>2145.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>97.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>8.600000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>19.000000</td>\n","      <td>25.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>97.000000</td>\n","      <td>173.200000</td>\n","      <td>65.500000</td>\n","      <td>54.100000</td>\n","      <td>2414.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>120.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>9.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>24.000000</td>\n","      <td>30.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>102.400000</td>\n","      <td>183.100000</td>\n","      <td>66.900000</td>\n","      <td>55.500000</td>\n","      <td>2935.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>141.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>9.400000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>30.000000</td>\n","      <td>34.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>3.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>120.900000</td>\n","      <td>208.100000</td>\n","      <td>72.300000</td>\n","      <td>59.800000</td>\n","      <td>4066.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>326.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>23.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>49.000000</td>\n","      <td>54.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         symboling normalized-losses    make  ...    city-mpg highway-mpg price\n","count   205.000000               205     205  ...  205.000000  205.000000   205\n","unique         NaN                52      22  ...         NaN         NaN   187\n","top            NaN                 ?  toyota  ...         NaN         NaN     ?\n","freq           NaN                41      32  ...         NaN         NaN     4\n","mean      0.834146               NaN     NaN  ...   25.219512   30.751220   NaN\n","std       1.245307               NaN     NaN  ...    6.542142    6.886443   NaN\n","min      -2.000000               NaN     NaN  ...   13.000000   16.000000   NaN\n","25%       0.000000               NaN     NaN  ...   19.000000   25.000000   NaN\n","50%       1.000000               NaN     NaN  ...   24.000000   30.000000   NaN\n","75%       2.000000               NaN     NaN  ...   30.000000   34.000000   NaN\n","max       3.000000               NaN     NaN  ...   49.000000   54.000000   NaN\n","\n","[11 rows x 26 columns]"]},"execution_count":22,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["# describe all the columns in \"df\" \n","df.describe(include = \"all\")"]},{"cell_type":"markdown","metadata":{"id":"b_yx-oiK7FLq"},"source":["<p>\n","Now, it provides the statistical summary of all the columns, including object-typed attributes.<br>\n","We can now see how many unique values, which is the top value and the frequency of top value in the object-typed columns.<br>\n","Some values in the table above show as \"NaN\", this is because those numbers are not available regarding a particular column type.<br>\n","</p>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173},"executionInfo":{"elapsed":3132,"status":"ok","timestamp":1577110666520,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"HjckIL3AgrjP","outputId":"bd8b83ee-f63a-489f-c094-f585d4db43a2"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>normalized-losses</th>\n","      <th>make</th>\n","      <th>fuel-type</th>\n","      <th>aspiration</th>\n","      <th>num-of-doors</th>\n","      <th>body-style</th>\n","      <th>drive-wheels</th>\n","      <th>engine-location</th>\n","      <th>engine-type</th>\n","      <th>num-of-cylinders</th>\n","      <th>fuel-system</th>\n","      <th>bore</th>\n","      <th>stroke</th>\n","      <th>horsepower</th>\n","      <th>peak-rpm</th>\n","      <th>price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","      <td>205</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>52</td>\n","      <td>22</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>8</td>\n","      <td>39</td>\n","      <td>37</td>\n","      <td>60</td>\n","      <td>24</td>\n","      <td>187</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>?</td>\n","      <td>toyota</td>\n","      <td>gas</td>\n","      <td>std</td>\n","      <td>four</td>\n","      <td>sedan</td>\n","      <td>fwd</td>\n","      <td>front</td>\n","      <td>ohc</td>\n","      <td>four</td>\n","      <td>mpfi</td>\n","      <td>3.62</td>\n","      <td>3.40</td>\n","      <td>68</td>\n","      <td>5500</td>\n","      <td>?</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>41</td>\n","      <td>32</td>\n","      <td>185</td>\n","      <td>168</td>\n","      <td>114</td>\n","      <td>96</td>\n","      <td>120</td>\n","      <td>202</td>\n","      <td>148</td>\n","      <td>159</td>\n","      <td>94</td>\n","      <td>23</td>\n","      <td>20</td>\n","      <td>19</td>\n","      <td>37</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       normalized-losses    make fuel-type  ... horsepower peak-rpm price\n","count                205     205       205  ...        205      205   205\n","unique                52      22         2  ...         60       24   187\n","top                    ?  toyota       gas  ...         68     5500     ?\n","freq                  41      32       185  ...         19       37     4\n","\n","[4 rows x 16 columns]"]},"execution_count":23,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df.describe(include='object')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":221},"executionInfo":{"elapsed":3068,"status":"ok","timestamp":1577110666522,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"ydVWB_dQhY2I","outputId":"e20042bc-7161-4d18-e962-13d67358c2ab"},"outputs":[{"data":{"text/plain":["0        std\n","1        std\n","2        std\n","3        std\n","4        std\n","       ...  \n","200      std\n","201    turbo\n","202      std\n","203    turbo\n","204    turbo\n","Name: aspiration, Length: 205, dtype: object"]},"execution_count":24,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df['aspiration'] # series"]},{"cell_type":"markdown","metadata":{"id":"TYAM0AQI7FLt"},"source":["<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n","<h1> Question #3: </h1>\n","\n","<p>\n","You can select the columns of a data frame by indicating the name of  each column, for example, you can select the three columns as follows:\n","</p>\n","<p>\n","    <code>dataframe[[' column 1 ',column 2', 'column 3']]</code>\n","</p>\n","<p>\n","Where \"column\" is the name of the column, you can apply the method  \".describe()\" to get the statistics of those columns as follows:\n","</p>\n","<p>\n","    <code>dataframe[[' column 1 ',column 2', 'column 3'] ].describe()</code>\n","</p>\n","\n","Apply the  method to \".describe()\" to the columns 'length' and 'compression-ratio'.\n","</div>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"elapsed":3030,"status":"ok","timestamp":1577110666523,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"w2Xzjgq27FLw","outputId":"fc2aac45-91e0-483c-83ab-0d44020cf80e"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>length</th>\n","      <th>compression-ratio</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>205.000000</td>\n","      <td>205.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>174.049268</td>\n","      <td>10.142537</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>12.337289</td>\n","      <td>3.972040</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>141.100000</td>\n","      <td>7.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>166.300000</td>\n","      <td>8.600000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>173.200000</td>\n","      <td>9.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>183.100000</td>\n","      <td>9.400000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>208.100000</td>\n","      <td>23.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           length  compression-ratio\n","count  205.000000         205.000000\n","mean   174.049268          10.142537\n","std     12.337289           3.972040\n","min    141.100000           7.000000\n","25%    166.300000           8.600000\n","50%    173.200000           9.000000\n","75%    183.100000           9.400000\n","max    208.100000          23.000000"]},"execution_count":25,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df[['length','compression-ratio']].describe()"]},{"cell_type":"markdown","metadata":{"id":"K6QBQWZJ7FL5"},"source":["Double-click <b>here</b> for the solution.\n","\n","<!-- The answer is below:\n","\n","df[['length', 'compression-ratio']].describe()\n","\n","-->\n"]},{"cell_type":"markdown","metadata":{"id":"Fr_qNtOs7FL8"},"source":["<h2>Info</h2>\n","Another method you can use to check your dataset is:"]},{"cell_type":"markdown","metadata":{"id":"bDCadRDa7FL-"},"source":["dataframe.info"]},{"cell_type":"markdown","metadata":{"id":"_u3UfwJB7FL_"},"source":["It provide a concise summary of your DataFrame."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":544},"executionInfo":{"elapsed":3010,"status":"ok","timestamp":1577110666525,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"Ru8N33Ox7FMB","outputId":"e6b63dd3-06fc-46d5-a373-32de03dedfb9"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 205 entries, 0 to 204\n","Data columns (total 26 columns):\n","symboling            205 non-null int64\n","normalized-losses    205 non-null object\n","make                 205 non-null object\n","fuel-type            205 non-null object\n","aspiration           205 non-null object\n","num-of-doors         205 non-null object\n","body-style           205 non-null object\n","drive-wheels         205 non-null object\n","engine-location      205 non-null object\n","wheel-base           205 non-null float64\n","length               205 non-null float64\n","width                205 non-null float64\n","height               205 non-null float64\n","curb-weight          205 non-null int64\n","engine-type          205 non-null object\n","num-of-cylinders     205 non-null object\n","engine-size          205 non-null int64\n","fuel-system          205 non-null object\n","bore                 205 non-null object\n","stroke               205 non-null object\n","compression-ratio    205 non-null float64\n","horsepower           205 non-null object\n","peak-rpm             205 non-null object\n","city-mpg             205 non-null int64\n","highway-mpg          205 non-null int64\n","price                205 non-null object\n","dtypes: float64(5), int64(5), object(16)\n","memory usage: 41.8+ KB\n"]}],"source":["# look at the info of \"df\"\n","df.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"elapsed":2990,"status":"ok","timestamp":1577110666526,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"jIU5BSsgwtRr","outputId":"5ae8745d-a567-44af-a4bc-d4c8a0b44816"},"outputs":[{"data":{"text/plain":["5170"]},"execution_count":27,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df['city-mpg'].sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":68},"executionInfo":{"elapsed":1072,"status":"ok","timestamp":1579410700057,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"eROb7hP1Xy_h","outputId":"a41acb2c-716e-44b5-f2af-bd364e700128"},"outputs":[{"data":{"text/plain":["city-mpg       5170\n","highway-mpg    6304\n","dtype: int64"]},"execution_count":10,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["df[['city-mpg','highway-mpg']].sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"elapsed":1553,"status":"ok","timestamp":1577110957911,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"VS_UxvrvH3IR","outputId":"8ad202d0-bc6a-4c7a-c52d-3a687c9d03f2"},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f18fd7a3240>"]},"execution_count":38,"metadata":{"tags":[]},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["# df['width'].plot(kind='kde')\n","df.width.plot(kind='kde')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":324},"executionInfo":{"elapsed":1603,"status":"ok","timestamp":1579411452200,"user":{"displayName":"StormlightX","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA7ffNh8zYCrByq7g6_RvAY6-iNKnGtjnQd5jEkklA=s64","userId":"04862885639735295114"},"user_tz":-330},"id":"fxZWbGYnXy6P","outputId":"1d56b8a0-e07c-420a-a786-f27713c1ca45"},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fdc440056d8>"]},"execution_count":17,"metadata":{"tags":[]},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 720x360 with 2 Axes>"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["\n","df.plot(x='width',y='height',\n","        kind='scatter',\n","        c=df['curb-weight'],\n","        s=df['height'],\n","        cmap='viridis',figsize=(10,5))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I7VnPvtcXy1k"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W1-EJauyXyxm"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uBiwHw62Xyr5"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uCzs2RoQXyoB"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W3tgzG-jXyjr"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3vPQ6f90Xyb6"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z98AZMvmXyYC"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IxYflZ2sXyUI"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"jApz2Vwe7FMG"},"source":["<p>\n","Here we are able to see the information of our dataframe, with the top 30 rows and the bottom 30 rows.\n","<br><br>\n","And, it also shows us the whole data frame has 205 rows and 26 columns in total.\n","</p>"]},{"cell_type":"markdown","metadata":{"id":"f63yuBTk7FMI"},"source":["<h1>Excellent! You have just completed the  Introduction  Notebook!</h1>"]}],"metadata":{"anaconda-cloud":{},"colab":{"collapsed_sections":[],"name":"Introduction.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"}},"nbformat":4,"nbformat_minor":0}